The goal of this research is to discover the cortical computations embodied in the responses of neurons in ventral visual area V2. Since V2 is heavily dependent on input from V1 for its visual responsiveness, we will develop a two-stage model, in which responses are constructed from a suitable combination of V1 afferents, with the design of each stage following a common canonical form. This model is intended to account for the visual response properties of neurons as economically as possible, while not necessarily reflecting the details of neuronal circuitry. This simplicity is deliberate, as it will allow the mdel to be fit to data recorded from single neurons, and, when assembled into a population, to predict perceptual capabilities. The motivation for the structure of the model, and our confidence in its success, comes from the convergence of three strands of previous work: (1) we have developed, fit, and validated a similar two-stage model for neuronal responses in area MT, a dorsal stream area which also receives primary afferent drive from area V1; (2) we have developed a two-stage model for visual texture representation that captures perceptually recognizable structures of natural images using spatial integration regions matched in size to those of V2 cells. We've shown that images synthesized to have matching model responses are indistinguishable to human observers; and (3) we've obtained preliminary data indicating that most of V2 cells respond more vigorously to synthetic texture stimuli than to spectrally matched noise stimuli, whereas V1 cells do not. The research is divided into three parts. First, we will gather electrophysiological data to dissect those model-generated features that underlie the increased responsiveness of V2 to texture stimuli. We will, in parallel, gather evidence for the increased responsiveness using fMRI, which will allow us to compare simultaneously measured responses averaged over neural populations in V1 and V2. Second, we will develop a physiologically plausible instantiation of the texture model and develop the methods to fit it to data from single neurons. Finally, we will link the novel functional response properties we have discovered to perception by simultaneously measuring neuronal responses and perceptual judgments in awake behaving macaques. To explore sensitivity to naturalistic features, we will relate psychometric and single-neuron neurometric functions, and use choice probability to link responses to behavioral performance.